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Description
MARCA OLIVETTI MODELO ET-2250
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.205.0192.00
Each circle represents one of the 2 purchases of CINTA CORRECTORA PARA MAQUINA DE ESCRIBIR ELECTRICA made by the IMSS between 2009 and 2013.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Norte del DF Almacen Delegacional En El D.f. Zona Norte | Ciudad de México | 1 | 303.6 MXN | 39.6 MXN | 15 % | 1.8 | 1 de enero de 2009 |
| Delegación San Luis Potosí Almacen Delegacional En San Luis Potosi | San Luis Potosi | 1 | 143.6 MXN | 19.8 MXN | 16 % | 1.92 | 1 de marzo de 2013 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| DEPORTES Y REGALOS SAMAR, S. A. DE C. V. | 1 | 39.6 MXN | 15 % | 1.8 | 2009 - 2009 |
| PAPELERIA FOYO S. DE R.L. DE C.V. | 1 | 19.8 MXN | 16 % | 1.92 | 2013 - 2013 |
Entity type
Utiles de oficina
Date updated in QQW
3 de octubre de 2022
Source(s)
Part of the project
Organization responsible for the import
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Add this to your code:
library(rjson)
#Read JSON data into R
data <- fromJSON(file="http://localhost:10010/v3/products?limit=1&sort=&embed=true&db_prefix=&id=312.205.0192.00")
Add this to your code:
import json, urllib
#Read JSON data into python
response = urllib2.urlopen('http://localhost:10010/v3/products?limit=1&sort=&embed=true&db_prefix=&id=312.205.0192.00')
data = json.loads(response.read())
Add this to your code:
import pandas as pd
#Read JSON data into pandas
df = pd.read_json('http://localhost:10010/v3/products?limit=1&sort=&embed=true&db_prefix=&id=312.205.0192.00')